



"""simple docstring"""





import random
def 						__lowerCAmelCase    (		__UpperCamelCase   :      int       ,			__UpperCamelCase   :      float       ,			__UpperCamelCase   :      bool = False     ):


						'''simple docstring'''






						snake_case_ :			dict            =      {i: [] for i in range(__UpperCamelCase     )}

						# if probability is greater or equal than 1, then generate a complete graph
						if probability >= 1:
												return complete_graph(__UpperCamelCase     )
						# if probability is lower or equal than 0, then return a graph without edges
						if probability <= 0:
												return graph

						# for each couple of nodes, add an edge from u to v
						# if the number randomly generated is greater than probability probability
						for i in range(__UpperCamelCase     ):
												for j in range(i + 1       ,			__UpperCamelCase     ):
																		if random.random() < probability:
																								graph[i].append(__UpperCamelCase     )
																								if not directed:
																														# if the graph is undirected, add an edge in from j to i, either
																														graph[j].append(__UpperCamelCase     )
						return graph
def 						__lowerCAmelCase    (		__UpperCamelCase   :      int     ):


						'''simple docstring'''






						return {
						    i: [j for j in range(__UpperCamelCase     ) if i != j] for i in range(__UpperCamelCase     )
						}


if __name__ == "__main__":
					import doctest

					doctest.testmod()




import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
		import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin







@require_torch
class 					__lowercase  (						unittest.TestCase		,       _SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							def       snake_case			(					self						)		->       Optional[Any]:
														A      :					Optional[Any]      		=					load_tool('''text-to-speech'''						)
														self.tool.setup()




							def       snake_case			(					self						)		->       Union[str, Any]:
														# SpeechT5 isn't deterministic
														torch.manual_seed(0						)
														A      :					Optional[Any]      		=					self.tool('''hey'''						)
														A      :					List[str]      		=					result.to_raw()
														self.assertTrue(
														    torch.allclose(
														        resulting_tensor[:3]    ,						torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]						)    ,						)						)






							def       snake_case			(					self						)		->       Optional[Any]:
														# SpeechT5 isn't deterministic
														torch.manual_seed(0						)
														A      :					int      		=					self.tool('''hey'''						)
														A      :					Any      		=					result.to_raw()
														self.assertTrue(
														    torch.allclose(
														        resulting_tensor[:3]    ,						torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]						)    ,						)						)






